Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7e85e5c7",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e6a93f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e723f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('train (1).csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "047aac2b",
   "metadata": {},
   "source": [
    "#### How Big Is The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a512ce44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37e2f229",
   "metadata": {},
   "source": [
    "#### How Does The Data Look Like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "faf9b2f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d5d21eba",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>577</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Garside, Miss. Ethel</td>\n",
       "      <td>female</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>243880</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>431</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bjornstrom-Steffansson, Mr. Mauritz Hakan</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110564</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C52</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>414</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Cunningham, Mr. Alfred Fleming</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239853</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>207</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Backstrom, Mr. Karl Alfred</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3101278</td>\n",
       "      <td>15.8500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>639</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Panula, Mrs. Juha (Maria Emilia Ojala)</td>\n",
       "      <td>female</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3101295</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "576          577         1       2                       Garside, Miss. Ethel   \n",
       "430          431         1       1  Bjornstrom-Steffansson, Mr. Mauritz Hakan   \n",
       "413          414         0       2             Cunningham, Mr. Alfred Fleming   \n",
       "206          207         0       3                 Backstrom, Mr. Karl Alfred   \n",
       "638          639         0       3     Panula, Mrs. Juha (Maria Emilia Ojala)   \n",
       "\n",
       "        Sex   Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "576  female  34.0      0      0   243880  13.0000   NaN        S  \n",
       "430    male  28.0      0      0   110564  26.5500   C52        S  \n",
       "413    male   NaN      0      0   239853   0.0000   NaN        S  \n",
       "206    male  32.0      1      0  3101278  15.8500   NaN        S  \n",
       "638  female  41.0      0      5  3101295  39.6875   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af22175e",
   "metadata": {},
   "source": [
    "#### Data Type Of The Columns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d876bbd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32a01f8c",
   "metadata": {},
   "source": [
    "#### Missing Values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0e4e47ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45ef558f",
   "metadata": {},
   "source": [
    "#### How Does The Data Look Mathematically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "441d6308",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6e9c070",
   "metadata": {},
   "source": [
    "#### Are There Duplicate Values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "36ab95e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4b30406",
   "metadata": {},
   "source": [
    "#### How Is The Correlation Between Columns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "66b3860a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId   -0.005007\n",
       "Survived       1.000000\n",
       "Pclass        -0.338481\n",
       "Age           -0.077221\n",
       "SibSp         -0.035322\n",
       "Parch          0.081629\n",
       "Fare           0.257307\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Survived']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "501428ff",
   "metadata": {},
   "source": [
    "### Categorical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c983bb70",
   "metadata": {},
   "source": [
    "#### Countplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d9ed178a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d6b5da16",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GIHAN SACHITH\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dee24ea2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6b6b9622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Survived'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dacee0aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GIHAN SACHITH\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Pclass'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eebe1798",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Survived'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Survived'].value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7c357f9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Survived'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Survived'].value_counts().plot(kind='pie',autopct='%.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b470ccc3",
   "metadata": {},
   "source": [
    "### Numerical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac4c0dc6",
   "metadata": {},
   "source": [
    "#### Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3d511741",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3f23277f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 54.,  46., 177., 169., 118.,  70.,  45.,  24.,   9.,   2.]),\n",
       " array([ 0.42 ,  8.378, 16.336, 24.294, 32.252, 40.21 , 48.168, 56.126,\n",
       "        64.084, 72.042, 80.   ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6306f3a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([24.,  6., 14.,  3.,  7.,  8.,  6.,  3.,  7., 22., 13., 51., 40.,\n",
       "        27., 46., 24., 36., 47., 27., 35., 17., 34., 29., 11., 27.,  8.,\n",
       "        18.,  9., 17., 18.,  6., 17.,  6.,  9.,  7.,  2.,  7.,  4.,  7.,\n",
       "         4.,  3.,  1.,  0.,  2.,  3.,  0.,  1.,  0.,  0.,  1.]),\n",
       " array([ 0.42  ,  2.0116,  3.6032,  5.1948,  6.7864,  8.378 ,  9.9696,\n",
       "        11.5612, 13.1528, 14.7444, 16.336 , 17.9276, 19.5192, 21.1108,\n",
       "        22.7024, 24.294 , 25.8856, 27.4772, 29.0688, 30.6604, 32.252 ,\n",
       "        33.8436, 35.4352, 37.0268, 38.6184, 40.21  , 41.8016, 43.3932,\n",
       "        44.9848, 46.5764, 48.168 , 49.7596, 51.3512, 52.9428, 54.5344,\n",
       "        56.126 , 57.7176, 59.3092, 60.9008, 62.4924, 64.084 , 65.6756,\n",
       "        67.2672, 68.8588, 70.4504, 72.042 , 73.6336, 75.2252, 76.8168,\n",
       "        78.4084, 80.    ]),\n",
       " <BarContainer object of 50 artists>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Age'],bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce5145af",
   "metadata": {},
   "source": [
    "#### Displot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "26089c23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1cd9163b970>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "59c51b01",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GIHAN SACHITH\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Density'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "72377134",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GIHAN SACHITH\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Density'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Pclass'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f23d97f6",
   "metadata": {},
   "source": [
    "#### Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d5e8da3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GIHAN SACHITH\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Fare'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['Fare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "714c0e87",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GIHAN SACHITH\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145510d8",
   "metadata": {},
   "source": [
    "#### Get Min,Max,Mean,Skew Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ed3ac436",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "06e81f43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d2d6efdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "590bf390",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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